University of Kansas summer stats camp

The Center for Research Methods and Data Analysis at the University of Kansas will host the Summer Statistical Institute (Stats Camp) from May 21 through June 8, 2018. Please visit the Web homepage for this event at http://crmda.ku.edu/statscamp.

These sessions are a combination of lecture format presentations and workshops for “hands on” practice. We recommend everybody should bring a laptop computer on which they have administrative privileges so that new software can be installed. There will be plenty of workshop “helpers” to troubleshoot problems during the workshop sessions. We have experience with Linux, MS Windows, and Macintosh computers (and configuration advice on http://crmda.ku.edu/setup).

This year, we have some new features, including

1. Remote access via Zoom for people who cannot attend in person

2. A new series of sessions on using Python for data acquisition and analysis.

The web page will have more details, but here is a brief summary.

Week 1: May 21-25. Using R (internally referred to as the “summeR workshop”). Covers the basics of interacting with R, importing data, creating graphics, and conducting statistical analysis. This will introduce tools for project management that are offered in our R package, “kutils”. Paul Johnson, CRMDA Director, is the leader on R.

Week 2: May 29-June 1. Python Data Science. As in the R workshop, covers basics of interacting with Python, including how to navigate around Jupyter Notebooks, working with text data, using Pandas and scraping Internet data. Jonathan Lamb, Associate Professor of English, is the leader on Python

Week 3: June 4-8. Structural Equation Modeling. The SEM overview will be offered by Professor Edward Merkle of the University of Missouri. The “SEM example” archive created by CRMDA (https://gitlab.crmda.ku.edu/crmda/semexample) will be introduced. The structural equations material, for the most part, uses R (and some Mplus) and it builds on the R modeling concepts discussed during the first week of the workshop. We will look into what to do when there is missing data, as well move into Bayesian methods for estimating SEM. We’ve also started a collection of TikZ illustrations (https://gitlab.crmda.ku.edu/crmda/tikz) and we’ll be glad to help if attendees are interested in learning about that.